Image gradients (Sobel, Laplacian) in Computer Vision - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to read an image using OpenCV.

Computer Vision

import cv2

image = cv2.[1]('image.jpg', cv2.IMREAD_GRAYSCALE)

Drag options to blanks, or click blank then click option'

AcvtColor

Bimwrite

Cimshow

Dimread

Attempts:

3 left

💡 Hint

Common Mistakes

Using cv2.imshow instead of cv2.imread

Using cv2.imwrite which saves images instead of reading

Using cv2.cvtColor which converts color spaces

The cv2.imread function reads an image from a file. Here, we read it in grayscale mode.

2fill in blank

medium

Complete the code to apply the Sobel operator to find the gradient in the x direction.

Computer Vision

sobelx = cv2.Sobel(image, cv2.CV_64F, [1], 0, ksize=3)

Drag options to blanks, or click blank then click option'

A1

B0

C-1

D2

Attempts:

3 left

💡 Hint

Common Mistakes

Setting dx to 0 which means no derivative in x

Using negative values which are invalid here

Confusing dx and dy arguments

The third argument is dx, which is 1 to compute the gradient in the x direction.

3fill in blank

hard

Fix the error in the code to compute the Laplacian of the image.

Computer Vision

laplacian = cv2.Laplacian(image, [1], ksize=3)

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Acv2.CV_64F

Bcv2.COLOR_BGR2GRAY

Ccv2.CV_8U

Dcv2.IMREAD_COLOR

Attempts:

3 left

💡 Hint

Common Mistakes

Using color conversion codes instead of depth

Using 8-bit unsigned depth causing incorrect results

The depth should be cv2.CV_64F to avoid overflow and keep negative gradient values.

4fill in blank

hard

Fill in the blank to compute the gradient magnitude from Sobel x and y gradients.

Computer Vision

import numpy as np

magnitude = np.sqrt(np.square(sobelx) [1] np.square(sobely))
magnitude = np.uint8(magnitude)

Drag options to blanks, or click blank then click option'

A-

B+

C*

D/

Attempts:

3 left

💡 Hint

Common Mistakes

Subtracting squares which is mathematically incorrect

Multiplying or dividing squares which does not give magnitude

The gradient magnitude is the square root of the sum of squares of x and y gradients.

5fill in blank

hard

Fill all three blanks to create a dictionary with keys as gradient types and values as their computed arrays.

Computer Vision

gradients = {
    '[1]': sobelx,
    '[2]': sobely,
    '[3]': laplacian
}

Drag options to blanks, or click blank then click option'

ASobel_X

BSobel_Y

CLaplacian

DGradient

Attempts:

3 left

💡 Hint

Common Mistakes

Using generic keys like 'Gradient' for all

Mixing up keys and values

Not using strings as keys

The dictionary keys should clearly label each gradient type: 'Sobel_X', 'Sobel_Y', and 'Laplacian'.

Practice

1. What is the main purpose of using image gradients like Sobel and Laplacian in computer vision?

easy

A. To increase the color saturation of an image

B. To convert the image into grayscale

C. To blur the image and reduce noise

D. To detect edges by highlighting rapid changes in pixel brightness

Solution

1. Step 1: Understand what image gradients do

   Image gradients detect changes in pixel brightness, which correspond to edges in images.

2. Step 2: Match purpose with options

   Sobel and Laplacian filters highlight edges by showing where brightness changes quickly, not color or blur.

3. Final Answer:

   To detect edges by highlighting rapid changes in pixel brightness -> Option D

4. Quick Check:

   Image gradients = edge detection [OK]

Hint: Edges = brightness changes, gradients highlight these [OK]

Common Mistakes:

• Confusing edge detection with color changes
• Thinking gradients blur images
• Assuming gradients convert images to grayscale

2. Which of the following is the correct way to apply a Sobel filter in OpenCV to detect horizontal edges?

easy

A. cv2.Laplacian(image, cv2.CV_64F)

B. cv2.Sobel(image, cv2.CV_64F, 0, 1, ksize=3)

C. cv2.Sobel(image, cv2.CV_64F, 1, 0, ksize=3)

D. cv2.Canny(image, 100, 200)

Solution

1. Step 1: Recall Sobel filter parameters

   In OpenCV, Sobel's dx=1 and dy=0 detects horizontal edges (changes along x-axis).

2. Step 2: Match parameters to options

   cv2.Sobel(image, cv2.CV_64F, 1, 0, ksize=3) uses dx=1, dy=0, which is correct for horizontal edge detection.

3. Final Answer:

   cv2.Sobel(image, cv2.CV_64F, 1, 0, ksize=3) -> Option C

4. Quick Check:

   dx=1, dy=0 means horizontal edges [OK]

Hint: dx=1, dy=0 for horizontal Sobel edges [OK]

Common Mistakes:

• Swapping dx and dy values
• Using Laplacian instead of Sobel for directional edges
• Confusing Canny edge detector with Sobel

3. Given the following Python code using OpenCV, what will be the shape of the output image after applying the Laplacian filter?

import cv2
image = cv2.imread('photo.jpg', cv2.IMREAD_GRAYSCALE)
laplacian = cv2.Laplacian(image, cv2.CV_64F)
print(laplacian.shape)

medium

A. Same height and width as the input grayscale image

B. One channel smaller than input image

C. A 3-channel color image

D. A single pixel value

Solution

1. Step 1: Understand Laplacian output size

   Laplacian filter outputs an image of the same size as input, preserving height and width.

2. Step 2: Check input image type

   Input is grayscale (single channel), so output remains single channel with same dimensions.

3. Final Answer:

   Same height and width as the input grayscale image -> Option A

4. Quick Check:

   Laplacian output size = input size [OK]

Hint: Laplacian keeps image size same as input [OK]

Common Mistakes:

• Expecting output to have fewer channels
• Thinking Laplacian converts grayscale to color
• Assuming output is a single pixel value

4. You wrote this code to apply Sobel filter but get an error:

import cv2
image = cv2.imread('img.png')
sobel = cv2.Sobel(image, cv2.CV_64F, 1, 0)

What is the likely cause of the error?

medium

A. Image path is incorrect

B. Sobel filter cannot be applied to color images

C. cv2.CV_64F is not a valid depth argument

D. Missing kernel size parameter 'ksize' in cv2.Sobel call

Solution

1. Step 1: Check image loading

   cv2.imread('img.png') returns None if file does not exist, causing Sobel to fail.

2. Step 2: Validate other parameters

   Sobel works on color images channel-wise, CV_64F is valid, ksize defaults to 3.

3. Final Answer:

   Image path is incorrect -> Option A

4. Quick Check:

   Check image is not None after imread [OK]

Hint: Always check if cv2.imread returns None [OK]

Common Mistakes:

• Forgetting to verify image loaded
• Assuming Sobel cannot handle color images
• Believing ksize parameter is mandatory

5. You want to detect edges in all directions in a noisy grayscale image. Which approach is best to get clear edges while reducing noise?

hard

A. Apply Sobel filter directly without preprocessing

B. Apply Gaussian blur first, then use Laplacian filter

C. Use only Gaussian blur without edge detection

D. Apply Laplacian filter first, then Gaussian blur

Solution

1. Step 1: Understand noise effect on edge detection

   Noise causes false edges; smoothing reduces noise before edge detection.

2. Step 2: Choose correct filter order

   Applying Gaussian blur first smooths noise, then Laplacian detects edges in all directions clearly.

3. Final Answer:

   Apply Gaussian blur first, then use Laplacian filter -> Option B

4. Quick Check:

   Smooth then detect edges = clear edges [OK]

Hint: Blur noisy image before Laplacian for better edges [OK]

Common Mistakes:

• Applying edge detection before noise reduction
• Using Sobel only for all-direction edges
• Skipping noise reduction step